In ZSSR [1]}, the property of internal image statistics is used to train an image-specific CNN, where the training examples are extracted from the test image itself. In training phase, several LR-HR pairs are generated by using data augmentation, and a CNN is trained with these pairs. In test time, the LR image \(I_{LR}\)  is fed to the trained CNN as input to get the reconstructed image. In this process, the model makes full use of internal statistics of the image itself for self-learning. In SinGAN [2]}, an unconditional generative model with a pyramid of fully convolutional GANs is proposed to learn the internal patch distribution at different scales of the image. To make use of the recurrence of internal information, they upsampled the LR image several times (depending on the final scale) to obtain the final SR output.
